Non-invasive Disease Diagnosis Research Articles

First-in-human prospective trial of sonobiopsy in high-grade glioma patients using neuronavigation-guided focused ultrasound

Sonobiopsy is an emerging technology that combines focused ultrasound (FUS) with microbubbles to enrich circulating brain disease-specific biomarkers for noninvasive molecular diagnosis of brain diseases. Here, we report the first-in-human prospective trial of sonobiopsy in high-grade glioma patients to evaluate its feasibility and safety in enriching plasma circulating tumor biomarkers. A nimble FUS device integrated with a clinical neuronavigation system was used to perform sonobiopsy following an established clinical workflow for neuronavigation. Analysis of blood samples collected before and after FUS sonication showed that sonobiopsy enriched plasma circulating tumor DNA (ctDNA), including a maximum increase of 1.6-fold for the mononucleosome cell-free DNA (cfDNA) fragments (120–280 bp), 1.9-fold for the patient-specific tumor variant ctDNA level, and 5.6-fold for the TERT mutation ctDNA level. Histological analysis of surgically resected tumors confirmed the safety of the procedure. Transcriptome analysis of sonicated and nonsonicated tumor tissues found that FUS sonication modulated cell physical structure-related genes. Only 2 out of 17,982 total detected genes related to the immune pathways were upregulated. These feasibility and safety data support the continued investigation of sonobiopsy for noninvasive molecular diagnosis of brain diseases.

Evaluation of Dimercaptosuccinic Acid-Coated Iron Nanoparticles Immunotargeted to Amyloid Beta as MRI Contrast Agents for the Diagnosis of Alzheimer's Disease.

Nanoparticle-based magnetic contrast agents have opened the potential for magnetic resonance imaging (MRI) to be used for early non-invasive diagnosis of Alzheimer's disease (AD). Accumulation of amyloid pathology in the brain has shown association with cognitive decline and tauopathy; hence, it is an effective biomarker for the early detection of AD. The aim of this study was to develop a biocompatible magnetic nanoparticle targeted to amyloid beta (Aβ) plaques to increase the sensitivity of T2-weighted MRI for imaging of amyloid pathology in AD. We presented novel iron core-iron oxide nanoparticles stabilized with a dimercaptosuccinic acid coating and functionalized with an anti-Aβ antibody. Nanoparticle biocompatibility and cellular internalization were evaluated in vitro in U-251 glioblastoma cells using cellular assays, proteomics, and transmission electron microscopy. Iron nanoparticles demonstrated no significant in vitro cytotoxicity, and electron microscopy results showed their movement through the endocytic cycle within the cell over a 24 h period. In addition, immunostaining and bio-layer interferometry confirmed the targeted nanoparticle's binding affinity to amyloid species. The iron nanoparticles demonstrated favourable MRI contrast enhancement; however, the addition of the antibody resulted in a reduction in the relaxivity of the particles. The present work shows promising preliminary results in the development of a targeted non-invasive method of early AD diagnosis using contrast-enhanced MRI.

A highly sensitive and long-term stable wearable patch for continuous analysis of biomarkers in sweat.

Although increasing efforts have been devoted to the development of non-invasive wearable or stretchable electrochemical sweat sensors for monitoring physiological and metabolic information, most of them still suffer from poor stability and specificity over time and fluctuating temperatures. This study reports the design and fabrication of a long-term stable and highly sensitive flexible electrochemical sensor based on nanocomposite-modified porous graphene by simple and facile laser treatment for detecting biomarkers such as glucose in sweat. The laser-reduced and patterned stable conductive nanocomposite on the porous graphene electrode provides the resulting glucose sensor with an excellent sensitivity of 1317.69 μAmM-1cm-2 with an ultra-low limit of detection (LOD) of 0.079 μM. The sensor can also detect pH and exhibit extraordinary stability to maintain more than 91% sensitivity over 21 days in ambient conditions. Taken together with a temperature sensor based on the same material system, the dual glucose and pH sensor integrated with a flexible microfluidic sweat sampling network further results in accurate continuous on-body glucose detection calibrated by the simultaneously measured pH and temperature. The low-cost, highly sensitive, and long-term stable platform could facilitate and pave the way for the early identification and continuous monitoring of different biomarkers for non-invasive disease diagnosis and treatment evaluation.

Circulating Tumor Cells Adhesion: Application in Biosensors.

The early and non-invasive diagnosis of tumor diseases has been widely investigated by the scientific community focusing on the development of sensors/biomarkers that act as a way of recognizing the adhesion of circulating tumor cells (CTCs). As a challenge in this area, strategies for CTCs capture and enrichment currently require improvements in the sensors/biomarker's selectivity. This can be achieved by understanding the biological recognition factors for different cancer cell lines and also by understanding the interaction between surface parameters and the affinity between macromolecules and the cell surface. To overcome some of these concerns, electrochemical sensors have been used as precise, fast-response, and low-cost transduction platforms for application in cytosensors. Additionally, distinct materials, geometries, and technologies have been investigated to improve the sensitivity and specificity properties of the support electrode that will transform biochemical events into electrical signals. This review identifies novel approaches regarding the application of different specific biomarkers (CD44, Integrins, and EpCAm) for capturing CTCs. These biomarkers can be applied in electrochemical biosensors as a cytodetection strategy for diagnosis of cancerous diseases.

Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease.

Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies. The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models. In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state. Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases.

Ppb-level detection of trimethylamine as biomarker in exhaled gas based on MoO3/V2O5 hierarchical heterostructure

The toxic gas trimethylamine (TMA) with fishy odor can not only be used as an indicator for evaluating seafood spoilage, but also as a biomarker for trimethylaminuria and kidney disease. This paper constructs a ppb-level trimethylamine gas sensor that operates at room temperature based on MoO3/V2O5 nanocomposites, which were fabricated by hydrothermal synthesis process. The adsorption energy of TMA on sensitive materials was calculated through the density functional theory, and the sensing mechanism was explored by simulating the adsorption process of TMA on the sensitive materials. The response (11.37%) of MoO3/V2O5 composites to ultra-low concentration (20 ppb) TMA was better than that of pure MoO3 nanorods (5.49%) and V2O5 nanospheres (6.36%) at room temperature. Compared with pure MoO3 sensors, an n-n heterojunction is formed between MoO3 and V2O5, which enhances the sensing performance of pure sensing material for TMA. In addition, the detection of exhaled gas in simulated kidney disease patients indicates that this sensor has great potential as an effective TMA detection tool in the field of non-invasive disease diagnosis.

Portal Venous Pulsatility Index: A Novel Biomarker for Diagnosis of High-Risk NonAlcoholic Fatty Liver Dise

Objective: To assess the accuracy of portal vein pulsatility (VPI) for non-invasive diagnosis of high-risk non-alcoholic fatty liver disease (NAFLD).
 Study Design: Cross-sectional study.
 Place and Duration of Study: Pakistan Institute of Medical Science, Islamabad Pakistan, from Jan to May 2022.
 Methodology: Duplex Doppler ultrasound was used to examine the main portal vein in patients with a biopsy-proven Nonalcoholic fatty liver disease (NAFLD) diagnosis. Patients were evaluated by Doppler ultrasonography after fasting for four hours in accordance with standard methodology. The maximum (Vmax) and minimum (Vmin) blood velocities in the portal veins were measured using the spectral waveform. 
 Results: Out of 240 NAFLD patients, 102(42.50%) were males, and 138(57.50 %) were females. According to the Non-alcoholic Steatohepatitis clinical research network (NASH CRN) scoring system, 95(39.58%) patients had F0 disease, 80(33.33%) had F1, 30(12.5%) patients F2, 24(10%) patients had F3 and 11(4.58%) had F4. 50(53.76%) patients in the low-risk group and 43(46.24%) patients in the high-risk group had diabetes. Of 102 males, 73(71.56%) had low risk, and 29(28.44%) had high risk. While out of 138 females, 79(57.24%) females had low risk, and 59(42.76%) females had a high risk of NAFLD. Venous pulsatility index, NAFLD fibrosis score, FIB-4, BARD score, and APRL had statistically significant differences (p<0.05).
 Conclusion: Venous pulsatility index is a reliable marker for detecting high-risk NAFLD. It is a readily available non-invasive investigation and can be reliably used as a screening tool for detecting NAFLD in high-risk patients, avoiding invasive investigation.

Highly Strain-Stable Intrinsically Stretchable Olfactory Sensors for Imperceptible Health Monitoring.

Intrinsically stretchable gas sensors possess outstanding advantages in seamless conformability and high-comfort wearability for real-time detection toward skin/respiration gases, making them promising candidates for health monitoring and non-invasive disease diagnosis and therapy. However, the strain-induced deformation of the sensitive semiconductor layers possibly causes the sensing signal drift, resulting in failure in achievement of the reliable gas detection. Herein, a surprising result that the stretchable organic polymers present a universal strain-insensitive gas sensing property is shown. All the stretchable polymers with different degrees of crystallinity, including indacenodithiophene-benzothiadiazole (PIDTBT), diketo-pyrrolo-pyrrole bithiophene thienothiophene (DPPT-TT) and poly[4-(4,4-dihexadecyl-4H-cyclopenta[1,2-b:5,4-b']dithiophen-2-yl)-alt-[1,2,5]thiad-iazolo [3,4-c] pyridine] (PCDTPT), show almost unchanged gas response signals in the different stretching states. This outstanding advantage enables the intrinsically stretchable devices to imperceptibly adhere on human skin and well conform to the versatile deformations such as bending, twisting, and stretching, with the highly strain-stable gas sensing property. The intrinsically stretchable PIDTBT sensor also demonstrates the excellent selectivity toward the skin-emitted trimethylamine (TMA) gas, with a theoretical limit of detection as low as 0.3ppb. The work provides new insights into the preparation of the reliable skin-like gas sensors and highlights the potential applications in the real-time detection of skin gas and respiration gas for non-invasive medical treatment and disease diagnosis.

Upconversion Nanoparticles as Imaging Agents for Dental Caries.

Dental caries (cavities) is the most prevalent disease worldwide; however, current detection methods suffer from issues associated with sensitivity, subjective interpretations, and false positive identification of carious lesions. Therefore, there is a great need for the development of more sensitive, noninvasive imaging methods. The 30 nm core@shell NaYF4; Yb20%, Er2%@NaYF4 upconversion nanoparticles (UCNPs), exhibiting strong upconversion emission from erbium upon excitation at 975 nm, were used in the imaging of locations of demineralized enamel and oral biofilm formation for the detection of dental caries. UCNPs were modified with poly(acrylic acid) (PAA) or poly-d-lysine (PDL), and targeting peptides were conjugated to their surface with affinity for either hydroxyapatite (HA), the material dentin is composed of, or the caries causing bacteria Streptococcus mutans. A statistical difference in the binding of targeted vs nontargeted UCNPs to HA was observed after 15 min, using both upconversion fluorescence of UCNP (p < 0.001) and elemental analysis (p = 0.0091). Additionally, using the HA targeted UCNPs, holes drilled in the enamel of bovine teeth with diameters of 1.0 and 0.5 mm were visible by the green emission after a 20 min incubation with no observable nonspecific binding. A statistical difference was also observed in the binding of targeted versus nontargeted UCNPs to S. mutans biofilms. This difference was observed after 15 min, using the fluorescence measurements (p = 0.0125), and only 10 min (p < 0.001) using elemental analysis via ICP-OES measurements of Y3+ concentration present in the biofilms. These results highlight the potential of these UCNPs for use in noninvasive imaging diagnosis of oral disease.

Vers une généralisation du diagnostic prénatal non-invasif des maladies monogéniques ? État des lieux et perspectives

The screening of fetal aneuploidies and non-invasive prenatal diagnosis of monogenic diseases (NIPD-MD) both rely on the study of free fetal DNA in maternal circulation, but their respective rise was unequal. Development of NIPD-MD has taken longer as it represents a less attractive commercial dynamic for industry, but also because it usually involves the development of tailored tests specific to each pathogenic variant. We have carried out a review of the literature on the various indications and technologies involved in the use of NIPD-MM. We present its current implementation and its development in France. To date, NIPD-MD has been routinely offered in France for several years by the laboratories of the French NIPD-MD network but remains mostly limited to the exclusion of paternal or de novo variants, the exclusion DPNI-MD. Indeed, it is still difficult to study the transmission of maternal variants from circulating free DNA analysis, due to its biological complexity: coexistence and predominance of similar DNA sequences of maternal origin. Different strategies, either direct or indirect, are being evaluated to establish fetal status regardless of the parental origin of the disease or its transmission mode. The emergence of commercial screening solutions for monogenic diseases complements the arsenal of prenatal exploration tools for these diseases. The multitude of existing technologies and protocols may complicate the information provided during antenatal consultations, but mastery of know-how and knowledge of ethical issues of NIPD-MD will ensure optimal service and better management of pregnancies at risk of transmitting monogenic disease.

Coded aperture snapshot spectral imaging fundus camera

Spectral imaging holds great promise for the non-invasive diagnosis of retinal diseases. However, to acquire a spectral datacube, conventional spectral cameras require extensive scanning, leading to a prolonged acquisition. Therefore, they are inapplicable to retinal imaging because of the rapid eye movement. To address this problem, we built a coded aperture snapshot spectral imaging fundus camera, which captures a large-sized spectral datacube in a single exposure. Moreover, to reconstruct a high-resolution image, we developed a robust deep unfolding algorithm using a state-of-the-art spectral transformer in the denoising network. We demonstrated the performance of the system through various experiments, including imaging standard targets, utilizing an eye phantom, and conducting in vivo imaging of the human retina.

Immunological mechanisms underlying clinical phenotypes and noninvasive diagnosis of immune checkpoint inhibitor-induced kidney disease.

Immune checkpoint inhibitors (ICIs) have become a mainstay of cancer therapy, with over 80 FDA-approved indications. Used in a variety of settings and in combination with each other and with traditional chemotherapies, the hyperactive immune response induced by ICIs can often lead to immune-related adverse events in bystander normal tissues such as the kidneys, lungs, and the heart. In the kidneys, this immune-related adverse event manifests as acute interstitial nephritis (ICI-AIN). In the era of widespread ICI use, it becomes vital to understand the clinical manifestations of ICI-AIN and the importance of prompt diagnosis and management of these complications. In this review, we delve into the clinical phenotypes of ICI-AIN and how they differ from traditional drug-induced AIN. We also detail what is known about the mechanistic underpinnings of ICI-AIN and the important diagnostic and therapeutic implications behind harnessing those mechanisms to further our understanding of these events and to formulate effective treatment plans to manage ICI-AIN.

Advancing Raman spectroscopy of erythrocytes with 3D-printed acoustofluidic devices

Acoustofluidics is a technique that utilizes the forces produced by ultrasonic waves and fluid flows to manipulate cells or nano-/microparticles within microfluidic systems. In this study, we demonstrate the feasibility of performing the Raman analysis of living human erythrocytes (Erys) within a 3D-printed acoustofluidic device designed as a half-wavelength multilayer resonator. Experiments show that a stable and orderly Ery aggregate can be formed in the pressure nodal plane at the resonator's mid-height. This has a significant potential for improving the applicability of Raman spectroscopy in single Ery analysis, as evidenced by the acquisition of the spectrum of healthy and pre-heated Erys without substrate interference. Moreover, principal component analysis applied on the obtained spectra confirms the correct Ery group identification. Our study demonstrates that 3D-printed acoustofluidic devices can improve the accuracy and sensitivity of Raman spectroscopy in blood investigations, with potential clinical applications for noninvasive disease diagnosis and treatment monitoring.

Superior Accuracy of Aspergillus Plasma Cell-Free DNA Polymerase Chain Reaction Over Serum Galactomannan for the Diagnosis of Invasive Aspergillosis.

Invasive aspergillosis (IA) in immunocompromised hosts carries high morbidity and mortality. Diagnosis is often delayed because definitive diagnosis requires invasive specimen collection, while noninvasive testing with galactomannan is moderately accurate. Plasma cell-free DNA polymerase chain reaction (cfDNA PCR) represents a novel testing modality for the noninvasive diagnosis of invasive fungal disease (IFD). We directly compared the performance of Aspergillus plasma cfDNA PCR with serum galactomannan for the diagnosis of IA during routine clinical practice. We conducted a retrospective study of all patients with suspected IFD who had Aspergillus plasma cfDNA PCR testing at Stanford Health Care from 1 September 2020 to 30 October 2022. Patients were categorized into proven, probable, possible, and no IA based on the EORTC/MSG definitions. Primary outcomes included the clinical sensitivity and specificity for Aspergillus plasma cfDNA PCR and galactomannan. Overall, 238 unique patients with Aspergillus plasma cfDNA PCR test results, including 63 positives and 175 nonconsecutive negatives, were included in this study. The majority were immunosuppressed (89.9%) with 22.3% 30-day all-cause mortality. The overall sensitivity and specificity of Aspergillus plasma cfDNA PCR were 86.0% (37 of 43; 95% confidence interval [CI], 72.7-95.7) and 93.1% (121 of 130; 95% CI, 87.4-96.3), respectively. The sensitivity and specificity of serum galactomannan in hematologic malignancies/stem cell transplants were 67.9% (19 of 28; 95% CI, 49.3-82.1) and 89.8% (53 of 59; 95% CI, 79.5-95.3), respectively. The sensitivity of cfDNA PCR was 93.0% (40 of 43; 95% CI, 80.9-98.5) in patients with a new diagnosis of IA. Aspergillus plasma cfDNA PCR represents a more sensitive alternative to serum galactomannan for noninvasive diagnosis of IA.

Noninvasive identificational diagnosis of diabetic nephropathy and non-diabetic renal disease based on clinical characteristics of Traditional Chinese Medicine symptom pattern and conventional medicine.

To study the clinical characteristics relating to differential diagnosis of diabetic nephropathy (DN) and non-diabetic renal disease (NDRD). The subjects were patients with type 2 diabetes mellitus (T2DM) complicated with chronic kidney disease (CKD). Western medical history data and Traditional Chinese Medicine (TCM) symptom pattern were collected, and logistic regression was used to analyze. Blood deficiency pattern [odds ratio () = 2.269, 0.017] and stagnation pattern ( = 1.999, 0.041) are independently related to DN. TCM factors blood deficiency pattern and stagnation pattern are relating to differential diagnosis of DN and NDRD.

Liver Fat Scores for Noninvasive Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease in Epidemiological and Clinical Studies.

Nonalcoholic fatty liver disease (NAFLD) is strongly associated with the metabolic syndrome and type 2 diabetes and independently contributes to long-term complications. Being often asymptomatic but reversible, it would require population-wide screening, but direct diagnostics are either too invasive (liver biopsy), costly (MRI) or depending on the examiner's expertise (ultrasonography). Hepatosteatosis is usually accommodated by features of the metabolic syndrome (e.g. obesity, disturbances in triglyceride and glucose metabolism), and signs of hepatocellular damage, all of which are reflected by biomarkers, which poorly predict NAFLD as single item, but provide a cheap diagnostic alternative when integrated into composite liver fat indices. Fatty liver index, NAFLD LFS, and hepatic steatosis index are common and accurate indices for NAFLD prediction, but show limited accuracy for liver fat quantification. Other indices are rarely used. Hepatic fibrosis scores are commonly used in clinical practice, but their mandatory reflection of fibrotic reorganization, hepatic injury or systemic sequelae reduces sensitivity for the diagnosis of simple steatosis. Diet-induced liver fat changes are poorly reflected by liver fat indices, depending on the intervention and its specific impact of weight loss on NAFLD. This limited validity in longitudinal settings stimulates research for new equations. Adipokines, hepatokines, markers of cellular integrity, genetic variants but also simple and inexpensive routine parameters might be potential components. Currently, liver fat indices lack precision for NAFLD prediction or monitoring in individual patients, but in large cohorts they may substitute nonexistent imaging data and serve as a compound biomarker of metabolic syndrome and its cardiometabolic sequelae.

The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.

Innovative advancement of contact lenses for noninvasive diagnosis and therapy: A mini review

AbstractWith the growth of the market and the number of wearers, contact lenses have been widely used for vision correction and cosmetic purposes for a few years. The current study opens up a new path for their applications. On the one hand, contact lenses are ideal sensing platforms due to advances in the monitoring of relevant indicators in tears such as glucose and intraocular pressure. Aside from detecting a single analyte at a time, multifunctional contact lens sensors were proposed to monitor two or more indicators at the same time. Contact lenses, on the other hand, are also suitable drug delivery platforms due to their good biocompatibility and long contact duration with the cornea. Different drug delivery approaches are being investigated to increase drug duration and bioavailability. In addition, the integration of these novel drug delivery methods into wearable sensors has become a topic for a complete closed‐loop program for disease management. We began this review by summarizing the categories and properties of current contact lenses. Then, innovative applications of contact lenses such as biosensors and drug delivery systems were also summarized. In general, the current progress in the applications of contact lenses provides a new possibility for noninvasive diagnosis and therapy of ocular diseases.

Machine Learning-Based Label-Free SERS Profiling of Exosomes for Accurate Fuzzy Diagnosis of Cancer and Dynamic Monitoring of Drug Therapeutic Processes.

Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.

Coupling annealed silver nanoparticles with a porous silicon Bragg mirror SERS substrate and machine learning for rapid non-invasive disease diagnosis.

Ag2O-Ag-porous silicon Bragg mirror (PSB) composite SERS substrates were successfully synthesized by using a combination of electrochemical and thermochemical methods. Test results showed that the SERS signal increased and decreased as the annealing temperature used for the substrate increased, where the most intense SERS signal was obtained using a substrate annealed at 300°C. Stability test results showed substantial enhancement of the SERS signal intensity of the Ag2O-Ag-PSB composite one month after preparation compared with that of conventional Ag-PSB. We conclude that Ag2O nanoshells play an essential role in SERS signal enhancement. Ag2O prevents natural oxidation of Ag nanoparticles (AgNPs) and has a solid localized surface plasmon resonance (LSPR). SERS signal enhancement was tested using this substrate for serum from patients with Sjögren's syndrome (SS) and Diabetic nephropathy (DN), as well as from healthy controls (HC). SERS feature extraction was performed using principal component analysis (PCA). The extracted features were analyzed by a support vector machine (SVM) algorithm. Finally, a rapid screening model for SS and HC, as well as DN and HC, was developed and used to perform controlled experiments. The results showed that the diagnostic accuracy, sensitivity and selectivity for SERS technology combined with machine learning algorithms reached 90.7%, 93.4% and 86.7% for SS/HC and 89.3%, 95.6% and 80% for DN/HC, respectively. The results of this study show that the composite substrate has excellent potential to be developed into a commercially available SERS chip for medical testing.